Methods for interpreting and understanding deep neural networks
Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +4 more
TLDR
The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which the author provides theory, recommendations, and tricks, to make most efficient use of it on real data.About:
This article is published in Digital Signal Processing.The article was published on 2018-02-01 and is currently open access. It has received 1939 citations till now. The article focuses on the topics: Interpretability & Artificial neural network.read more
Citations
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Journal ArticleDOI
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Journal ArticleDOI
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Posted Content
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
Journal ArticleDOI
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Chou P. Hung,Brent J. Lance +7 more
TL;DR: This work introduces EEGNet, a compact convolutional neural network for EEG-based BCIs, and introduces the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.